Ronald J. Williams is professor of computer science at Northeastern University, and one of the pioneers of neural networks. He co-authored a paper on the backpropagation algorithm which triggered a boom in neural network research.[1] He also made fundamental contributions to the fields of recurrent neural networks[2][3] and reinforcement learning.[4] Together with Wenxu Tong and Mary Jo Ondrechen he developed Partial Order Optimum Likelihood (POOL), a machine learning method used in the prediction of active amino acids in protein structures. POOL is a maximum likelihood method with a monotonicity constraint and is a general predictor of properties that depend monotonically on the input features.[5]

References

  1. ↑ David E. Rumelhart, Geoffrey E. Hinton und Ronald J. Williams. Learning representations by back-propagating errors., Nature (London) 323, S. 533-536
  2. ↑ Williams, R. J. and Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1, 270-280.
  3. ↑ R. J. Williams and D. Zipser. Gradient-based learning algorithms for recurrent networks and their computational complexity. In Back-propagation: Theory, Architectures and Applications. Hillsdale, NJ: Erlbaum, 1994.
  4. ↑ Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8, 229-256.
  5. ↑ W. Tong, Y. Wei, L.F. Murga, M.J. Ondrechen, and R.J. Williams (2009). Partial Order Optimum Likelihood (POOL): Maximum Likelihood Prediction of Active Site Residues Using 3D Structure and Sequence Properties. PLoS Computational Biology, 5(1): e1000266.
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